Impaired homeostatic regulation of feedback inhibition associated with system deficiency to detect fluctuation in stimulus intensity: a simulation study

The dependence of neuronal encoding efficiency on Hebbian plasticity and homeostatic regulation of neurotransmitter release

“…Retrograde signaling in the absence of Hebbian learning rule can help neurons to control their spiking activities, but encoding efficiency does not reach high levels because release inhibitory activity is not strong enough to control spiking rate. Moreover, adding the simulation of effective inhibitory feedback by an inhibitory neuron on the network shows its vital role in encoding efficiency of single neurons when combined with Hebbian mechanism and retrograde messenger comparing to the efficiency in the absence of these synaptic mechanisms (Faghihi and Moustafa, 2015 ). These simulations assign a critical role for nitric oxide as a known retrograde messenger with desired properties for the proposed hypothesis.…”

“…For this purpose, information theory has proposed some measures including mutual information that can be measured by different approaches to study the role of structural and physiological parameters involved in neural systems of different senses (van Steveninck et al, 1997 ). Recently, a geometrical approach has been introduced that aims to measure neural system efficiency by the calculation of defined words in a given neural response (Faghihi and Moustafa, 2015 ). In the current study, this approach is used to measure the role of different parameters including different synapses in a single neuron's efficiency to encode its inputs.…”

“…This quantity is usually calculated using “mutual information” between a spike train and the stimulus as an information theoretic approach (Kumbhani et al, 2007 ; Faghihi et al, 2013 ; Fan, 2014 ; Jung et al, 2014 ). As neural systems should be able to detect a fluctuation in a stimulus intensity, a new encoding efficiency measure has been recently introduced which uses the geometric distance between stimulus and the response of a given neuron (Faghihi and Moustafa, 2015 ).…”

A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia

“…The connectivity rate between the EC and the DG is unknown so it is assumed as a model parameter as values between zero and one which is defined as the probability of connecting each neuron in second layer to neurons in the first layer. The inhibitory neuron with different inhibition intensity is modeled as in ( Faghihi and Moustafa, 2015 ). The activity of neurons in the first layer (the EC) is modeled as firing probability in each time bin.…”

“…Inhibitory neurons are found in all parts of the animal brain such that any impairment in their function is associated with psychiatric and neurological disorders ( Konradi et al, 2011 ). The role of the balance between excitation and inhibition has been shown using simulation studies to be critical in keeping the efficiency of neurons in response to presenting incremental stimulus intensity ( Faghihi and Moustafa, 2015 ).…”